Scalar Quantities

Visualize scalar valued data at the nodes or edges of a curve network.

Example:

import numpy as np
import polyscope as ps
ps.init()

# register a curve network 
N_node = 100
N_edge = 250
nodes = np.random.rand(N_node, 3)
edges = np.random.randint(0, N_node, size=(N_edge,2))
ps_net = ps.register_curve_network("my network", nodes, edges)

# visualize some random data per-node
vals_node = np.random.rand(N_node)
ps_net.add_scalar_quantity("rand vals", vals_node, enabled=True)

# visualize some random data per-edge
vals_edge = np.random.rand(N_edge)
ps_net.add_scalar_quantity("rand vals2", vals_edge, defined_on='edges')

# as always, we can customize the initial appearance
ps_net.add_scalar_quantity("rand vals2 opt", vals_edge, defined_on='edges', 
                           enabled=True, vminmax=(-3., 3.), cmap='reds')


# view the network with all of these quantities
ps.show() 

CurveNetwork.add_scalar_quantity(name, values, defined_on='nodes', enabled=None, datatype="standard", vminmax=None, cmap=None)

Add a scalar quantity to the network.

• name string, a name for the quantity
• values a length N numpy array, scalars at nodes/edges
• defined_on string, one of "nodes" or "edges", is this data a value per-node or a value per-edge?

This function also accepts optional keyword arguments listed below, which customize the appearance and behavior of the quantity.

Categorical Scalars

Scalar quantities can also be used to visualize integer-valued labels such as categories, classes, segmentations, flags, etc.

Add the labels as a scalar quantity where the values just happen to be integers (each integer represents a particular class or label), and set datatype='categorical'. This will change the visualization to a different set of defaults, adjust some shading rules, and use a distinct color from the colormap for each label.

Scalar Quantity Options

When adding a scalar quantity, the following keyword options can be set. These are available for all kinds of scalar quantities on all structures.

Keyword arguments:

• enabled boolean, whether the quantity is initially enabled (note that generally only one quantitiy can be shown at a time; the most recent will be used)
• datatype, one of "standard", "symmetric", "magnitude", or "categorical", affects default colormap and map range, and the categorical policies mentioned above
• vminmax, a 2-tuple of floats, specifying the min and max range for colormap limits; the default is None, which computes the min and max of the data
• cmap, which colormap to use
• isoline keywords (darker-shaded stripes showing isocontours of the scalar field):
  • isolines_enabled are isolines enabled (default: False)
  • isoline_style one of stripe, 'contour (default: stripe)
  • isoline_period how wide should the darkend stripes be, in data units (default: dynamically estimated)
  • isoline_period_relative if true, interpret the width value as relative to the world coordinate length scale (default: False)
  • isoline_darkness how much darker should the alternating stripes be (default: 0.7)
  • isoline_contour_thickness how thick should the contour lines be (default: 0.3)

If not specified, these optional parameters will assume a reasonable default value, or a persistent value if previously set.